INTRODUCTION

Alpine and Arctic ecosystems cover approximately 40% of the Earth land surface, contain high biodiversity and a variety of ecosystem functions and services such as water, soil, and nutrient retention and regulation, carbon sequestration and storage, grazing areas for wildlife and livestock and cultural and recreational services (Körner and Oshawa 2005). Grasslands in cold ecosystems are often semi-natural and have been managed for grazing and fodder production for centuries. These grasslands can be species rich in vascular plants due to their evolutionary history, specific abiotic conditions and land-use management (Poschlod et al. 1998; Merunkova et al. 2012). Due to low temperatures in these ecosystem, processes such as decomposition and respiration are slow resulting in potentially important carbon sinks (Kato et al. 2004; Schimel et al. 2002). Traditional land-use such as grazing (including both wildlife and livestock) and mowing has drastically changed during the last century to either increased intensification or abandonment and the implications for biodiversity and ecosystem functioning are diverse.

Grazing and mowing directly or indirectly affect vegetation, nutrient cycle, microclimate and soil in similar ways (except for additional nutrients and trampling for grazing) with impacts on biodiversity and ecosystem functioning (Figure @ref(fig:diagram)a, Wardle et al. 2004). Biodiversity in grasslands is influenced by plant productivity and biomass, and in an undisturbed vegetation, this relationship is hump-shaped (Figure @ref(fig:diagram)b; Noy-Meir 1975). To a certain threshold, higher productivity increases the amount of biomass, which generates a positive feedback to plant productivity, however beyond this threshold, the relationship reverses. In a grazed or mowed system, biomass is constantly removed, while grazing also affects productivity via nutrient input (i.e. dung and urine). The relationship between grazing and productivity is non-linear and often highest productivity is reached at an intermediate grazing level (Figure @ref(fig:diagram)c, McNaughton 1979; Milchunas and Lauenroth 1993; Austrheim et al. 2014, 2016). At a low grazing intensity, relative to productivity, competitive exclusion leads to dominance by a few species, whereas under very high grazing intensity, again relative to productivity, too much biomass is removed to the point where a system is degraded or reaches a tipping point (Grime 1973; Odriozola et al. 2017). The effect of grazing on biodiversity is however context-dependent, where both the intensity of grazing and the productivity of the system are important (Figure @ref(fig:diagram)d, Bernes et al. 2015; Collins et al. 1998). Colder and nutrient-poor ecosystems often support low-productivity plant communities with little biomass, but which are often relatively species rich (Grime 1973; Milchunas and Lauenroth 1993). Such communities are usually characterised by facilitative species interactions (Bertness and Callaway 1994) and consequently have a positive relationship between biomass and species diversity (Figure @ref(fig:diagram)b, left side of graph). In such systems, a reduction in biomass, for example via grazing, will result in a decrease in biodiversity (Choler, Michalet, and Callaway 2001; Bernes et al. 2015). More productive plant communities will result in increased competition for light among species and lower species diversity (Figure @ref(fig:diagram)b, right side of graph, Grime 1973; Collins et al. 1998; Bernes et al. 2015). Removal of biomass in a productive system can therefore be positive for biodiversity and the net effect of grazing on biodiversity will therefore switch along a productivity gradient.

Impact of grazing on the vegetation and soil. a) Pathways of the effect of grazing on vegetation, nutrient cycling, microclimate and soil with impacts on biodiversity and ecosystem carbon fluxes. b) Relationship between biomass/productivity and diversity in an ungrazed grassland. c) Relationship between grazing intensity and diversity. d) Impact of grazing intensity and habitat productivity on diversity.

Impact of grazing on the vegetation and soil. a) Pathways of the effect of grazing on vegetation, nutrient cycling, microclimate and soil with impacts on biodiversity and ecosystem carbon fluxes. b) Relationship between biomass/productivity and diversity in an ungrazed grassland. c) Relationship between grazing intensity and diversity. d) Impact of grazing intensity and habitat productivity on diversity.

Grazing and mowing also affect the soil structure and soil processes such as decomposition, soil respiration and soil carbon stocks which are linked to the carbon cycle (Figure @ref(fig:diagram)a). Directly grazers impact the soil via nutrient input or trampling (not considered in this study) and indirectly by removal of biomass, leading to change in vegetation structure, composition, affecting litter input, and microclimate (Zeeman et al. 2010). Grazers directly increase nutrient availability in the soil, which can accelerate processes such as decomposition or soil respiration (Frank et al. 2000). Indirectly, the loss of photosynthetic tissue by grazing or mowing (i.e. decrease in Leaf Area Index; LAI) can reduce the carbon acquisition (Owensby, Ham, and Auen 2006; Schmitt et al. 2010). Removal of biomass can also change the amount and quality of litter that enters the soil and thus indirectly change the decomposition rate (Sjögersten et al. 2011; Olofsson, Stark, and Oksanen 2004; Semmartin et al. 2004) and the amount of carbon added to the soil (Martinsen et al. 2011; Sun et al. 2011). As a response to grazing and/or mowing, plants can change their biomass allocation from shoots to roots or vice versa affecting the amount of carbon stored in the soil (Frank, Kuns, and Guido 2002; Archer and Tieszen 1983). The removal of biomass also alters the microclimate such as ground and soil temperature and soil moisture (Klein, Harte, and Zhao 2005; Shao et al. 2012; Olofsson, Stark, and Oksanen 2004), which can accelerate or slow down processes in the soil. Finally, land-use history is important for how much carbon has been accumulated in the soil and carbon effluxes (Welker et al. 2004; Ylänne and Stark 2019). Grazing and mowing thus affect the carbon cycle in multiple ways, and often carbon gas effluxes increase with grazing due to higher soil carbon, better microclimatic conditions for respiration (Zhang et al. 2015; Jun et al. 2008; Gao et al. 2009; Sagar et al. 2019), but not always (Welker et al. 2004). Similarly, to the impacts aboveground, grazing intensity and the environmental context, i.e. the productivity and climate are important (McSherry and Ritchie 2013; Tang et al. 2019). High grazing intensity has been shown to have a negative impact on the acquisition of soil carbon in low productive systems (e.g. dominated by C3 grasses) (McSherry and Ritchie 2013; Steffens et al. 2008; Golluscio et al. 2009), however opposite patterns have been explained by shifts in plant species composition and increased allocation in root biomass (Gao et al. 2009). An intermediate grazing level can have a positive effect on carbon cycling by increasing soil carbon stocks (Martinsen et al. 2011; Lu et al. 2017; Austrheim et al. 2016). Precipitation can increase carbon sequestration (Tang et al. 2019), however evidence from regions with high precipitation are missing (MAP > 1000 mm). The interplay between grazing and/or mowing intensity and ecosystem productivity on the carbon cycle are complex and not well understood.

Grazing and/or mowing impact the vegetation or soil processes simultaneously, however they are usually studied separately (but see Sagar et al. 2019; Austrheim et al. 2016). Functional diversity, by using plant functional traits is one way to link environmental drivers and plant responses to ecosystem functioning (Diaz, Noy-Meir, and Cabido 2001; Lavorel et al. 2007). (Need a transition here…) Grazing can reduce plant height, leaf area (i.e. smaller area for foraging) and leaf dry matter content (i.e. tougher leaves), reflecting a grazing avoidance strategy (Hui and Guoqi 2014), and can result in lower carbon acquisition in the plants (ref). Alternatively, grazing can stimulate plant growth (i.e. increased specific leaf area and turnover) and fresh leaves are more efficient in carbon uptake (Owensby, Ham, and Auen 2006). Thus, plant functional traits in response to grazing and/or mowing reflect a trade-off between the resource acquisition spectrum and a response to grazing.

Global change is profoundly impacting cold ecosystems and warmer climate and nitrogen addition are two key drivers (Pacifici et al. 2015; IPCC 2014; IPBES 2018). They act at larger spatial and temporal scales compared to grazing and mowing, but influence grassland biodiversity and ecosystem carbon cycling via similar pathways, i.e plant productivity, biomass, decomposition and soil respiration and carbon stocks. They increase nutrients availability directly or indirectly by elongating the growing season and/or increasing photosynthetic activity. Warming and nitrogen addition will therefore push a plant community towards more productive and competitive conditions, resulting in decreased biodiversity (Humbert et al. 2016; Elmendorf et al. 2012; Klanderud 2008). These drivers affect soil processes by increasing decomposition, soil respiration and sometimes accumulation of soil carbon (Gavazov2010-bz; Althuizen et al. 2018), which can result in higher ecosystem effluxes (Jun et al. 2008; Zhang et al. 2015). However the net effect of these global change drivers also depend on soil moisture, the vegetation and consequently the quality of the litter (e.g. Emmett et al. 2004). Overall, warming and nitrogen deposition pose a threat to alpine biodiversity and ecosystem functions and their combined effects with grazing are not well understood (but see Zhu et al. 2015).

Several meta-analysis have synthesized the effects of grazing and/or mowing on plant diversity (Herrero-Jáuregui and Oesterheld 2018; Gao and Carmel 2020; Tälle et al. 2016), productivity (Yan, Zhou, and Zhang 2013), soil carbon or soil quality (Xiong et al. 2016; Lai and Kumar 2020; Zhan et al. 2020) or carbon fluxes (Tang et al. 2019), focusing on grazing intensity (e.g. Herrero-Jáuregui and Oesterheld 2018; Yan, Zhou, and Zhang 2013; Zhan et al. 2020), grazing exclusion (e.g. Xiong et al. 2016) or the difference between grazing and mowing (Tälle et al. 2016). These studies focus on regional or global levels but we are not aware of any studying grazing and/or mowing intensity and productivity in alpine or arctic ecosystems (but see Bernes et al. 2015 on impacts of reindeer in cold ecosystems). Here, we conduct a meta analysis to estimate the effect of wildlife or livestock grazing and/or mowing on taxonomic or functional plant diversity and/or ecosystem carbon fluxes in alpine and arctic habitats. We focus on the role of grazing and/or mowing intensity and ecosystem productivity, and additionally explore if this effect can be explained by methodology, grazer identity, duration and timing of the treatment, biomass, decomposition, soil carbon, and soil depth. Specifically, we hypothesize that the interplay of grazing intensity and productivity define the net impact of grazing on biodiversity and ecosystem functioning. The impact of grazing and/or mowing will be negative in low productive habitats, and switch to positive with increasing productivity, but also depend strongly on the intensity of grazing and/or mowing. We hypothesize that an intermediate level of grazing/mowing is beneficial to maintain or increase biodiversity and soil carbon sequestration. Finally, we are interested how these effects change in combination with warmer climate and/or nitrogen addition. We hypothesize that warming and nitrogen addition will negatively impact biodiversity and ecosystem carbon fluxes, but grazing and/or mowing have the potential to mitigate those negative impacts (Martinsen et al. 2011; Austrheim et al. 2016; Smith et al. 2015).

METHODS

We use a systematic approach to screen the literature in an unbiased and transparent way and use best practice protocols to quantify the magnitude of the outcome and assess the source of heterogeneity across studies (Hillebrand and Gurevitch 2016).

Pilot study

We conducted a pilot study as recommended by Foo et al. (2021), on the 26th November 2020 including literature search, title, abstract and full text screening and data extraction. The pilot study helped finalizing the research question, select suitable search terms to reach an efficient hit rate (number of relevant studies during the screening process), defining the suitable inclusion and appraisal criteria, and relevant information and metrics for the data extraction as well as preparing the code to conduct the analyses and present the results. We used 10 % of the papers from the full literature search in web of science to conduct the pilot study and extracted data from 1.2 % of the papers from the full search.

Screening process

The original search resulted in 208 (note that these numbers are from the pilot study) unique articles (Figure @ref(fig:roses)) after the removal of 5 duplicates. A random selection of ca. 10% of the papers will be screened by two independent reviewers to check for consistency in the inclusion process. We will conducted a kappa analysis, which assesses the agreement for decision between reviewers (Pullin and Stewart 2006).

ROSES flow diagram for the systemtic literature search

ROSES flow diagram for the systemtic literature search

The full-text of the remaining 75 studies were assessed against the following inclusion and appraisal criteria (Table @ref(tab:criteria-table)). Studies conducted in (a) natural or semi-natural alpine or arctic/tundra grasslands with a (b) grazing, mowing or clipping treatment and a control (no treatment) were included. Studies also needed to report effects on (c.1) plant diversity (taxonomic or functional) and/or (c.2) ecosystem carbon fluxes using chamber measurements, biomass harvesting and/or soil sampling, number of replication and a measure of variance. Finally, studies that included a global change treatment (d) such as warming and/or nitrogen addition were included, however this criteria was not mandatory because we are interested in the grazing/mowing treatment alone and in combination with warming and/or nitrogen addition.

Data extraction and moderators

From each study, we extracted data on the a) study location, b) study design, c) ecological and d) response variables (Table @ref(tab:moderator-table)). We hypotheses that the moderators a - c) have an effect on the response variables d). For the response variables, we extracted the mean value for grazed/mowed (treatment) and control plots from each study site and if available also from the warming and nitrogen addition treatment. We also retrieved number of replication per treatment and a measure of variance (i.e. standard deviation, standard error or variance) for studies with multiple replication and calculated standard deviation. If the data was only available from figures, we used the R package metaDigitise to extract the data (Pick, Nakagawa, and Noble 2018).

62 studies were excluded because they were conducted in the wrong type of habitat (e.g. boreal forest, coastal grasslands), there was no grazing treatment, no response variable, no measure of variance for the response variable, no data available or the paper was not found (for details see Figure @ref(fig:roses)). Information that was missing but could be retrieved otherwise was added to the dataset, i.e. coordinates from site names and maps and grassland type from other papers. Mean annual temperature and annual precipitation for each sites was extracted using the CHELSEA climate database (Karger et al. 2017).

For studies that were conducted at more than one site (e.g. along environmental gradients) or over several years, we included all sites, but treated each site as independent record and used only the last year of data collection.

Calculation of the effect size

The meta analysis was conducted separately for the two response variables plant species diversity and ecosystem carbon fluxes. Effect size (D) was calculated for each study site using the raw mean difference between the control and the grazing or mowing treatment (Borenstein et al. 2011):

\[ D = X_T - X_C, \]

where XT and XC are the means of the two groups (treatment and control). For each of the studies the standard error of the effect size was calculated as:

\[ SE_D = \sqrt{\frac{n_T + n_C}{n_T n_C}} {S^2_{pooled}}, \]

where nT and nC are the sample sizes of the two groups (treatment and control), and S2pooled is the pooled standard deviation assuming that the standard deviation of the two groups are the same.

\[ S^2_{pooled} = \sqrt{\frac{(n_T-1)S_T^2 + (n_C -1)S_C^2}{n_T + n_C - 2}} \]

with ST and SC as the sample standard deviation of the two groups (treatment and control).

The treatment includes grazing, mowing, or clipping, while the control treatment is absence of grazing/mowing/clipping. A positive raw mean difference means that grazing or mowing has a positive effect on diversity or ecosystem functioning.

Effect sizes were calculated separately for each variable (i.e. richness, Shannon diversity index, GPP, NPP) because they have different units and are not comparable. If available, the effect size was also calculated for the warming and/or nitrogen addition treatment at each study site (see below).

Test grand-mean effect size and publication bias

To test if the grand-mean effect size of the treatment on plant species diversity or ecosystem carbon fluxes differed from zero, we used a random effects model with effect size of the different variables as response and study as random variable. We accounted for variation within studies (i.e. sampling error) and across studies, by weighing the effect size of each study by the inverse of the total variance (Borenstein et al. 2011). The grand mean effect size is considered to be significantly different from zero if the 95% confidence intervals does not contain zero.

The publication bias was assessed visually, by drawing funnel plots to check for asymmetry or gaps in the data (Sterne & Egger 2001), and statistically with Egger’s regression asymmetry test (Egger et al. 1997).

Heterogenity analysis

We conducted subgroup analysis for all categorical moderators (continent, biogegraphic region, grassland type, land-use history, treatment, experimental design, grazer and grazer intensity, timing of treatment) to estimate their effect size and compare across subgroups. The effect size for all quantitative moderators (climate, time since treatment, grazer density, treatment frequency, mowing/clipping height, plant productivity, biomass, decomposition, soil carbon and soil depth) was estimated using meta-regressions. The model contained effect size as response variable and one moderator as explanatory variable. Study ID was included as a random factor?

We hypothesized that the effect of grazing on diversity and ecosystem functioning depends on grazer intensity and productivity. We therefore tested the subgroup analysis and meta-regressions including separate models including grazer intensity and productivity.

Interaction og grazing and global change

On a subset of the data set that included both treatment (grazing and/or mowing) and one global change driver i.e. warming or nitrogen addition additional analyses were conducted. For each study we calculated the effect size (see above) for ambient temperature and warming or unfertilized and fertilized plots. The overall effect of grazing was calculated as the difference between the sum of the two treatments including grazers and the sum of the two treatments excluding grazers. We used a random effects model with effect size of the different variables as response and study as random variable to test if the grand mean effect sized differed from zero.

All analysis were done in R (R Core Team 2020) using the metafor package (Viechtbauer 2010). The extracted data from this meta analysis will be archived on an Open Science Framwork repository (https://osf.io/6da7t/) and the code for data handling, analysis and vizualization is available on a GitHub repository (https://github.com/audhalbritter/Grazing_metaanalysis).

Questions

Should we include studies with different intensities (i.e. low, mid and high grazing) that do not have a control?

Is productivity and grazing intensity a requirement, because we are interested in these variables. E.g. nr. of animals per area?

Do we want to test all the

Should we do more analysis for productivity and grazing intensity?

Functional diversity? How is that define? Do we need traits and then calculate a measure of diversity or we just call it fun div for now?

How can we test if there is a link between BD and EF?

REFERENCES

Althuizen, I H J, H Lee, J M Sarneel, and V Vandvik. 2018. “Long-Term Climate Regime Modulates the Impact of Short-Term Climate Variability on Decomposition in Alpine Grassland Soils.” Ecosystems 21 (8): 1580–92.

Archer, S, and L L Tieszen. 1983. “Effects of Simulated Grazing on Foliage and Root Production and Biomass Allocation in an Arctic Tundra Sedge (Eriophorum Vaginatum).” Oecologia 58 (1): 92–102.

Austrheim, G, J D M Speed, M Evju, A Hester, Ø Holand, L E Loe, V Martinsen, et al. 2016. “Synergies and Trade-Offs Between Ecosystem Services in an Alpine Ecosystem Grazed by Sheep – an Experimental Approach.” Basic Appl. Ecol. 17 (7): 596–608.

Austrheim, G, J D M Speed, V Martinsen, J Mulder, and A Mysterud. 2014. “Experimental Effects of Herbivore Density on Aboveground Plant Biomass in an Alpine Grassland Ecosystem.” Arct. Antarct. Alp. Res. 46 (3): 535–41.

Bernes, Claes, Kari Anne Bråthen, Bruce C Forbes, James D M Speed, and Jon Moen. 2015. “What Are the Impacts of Reindeer/Caribou (Rangifer Tarandus L.) on Arctic and Alpine Vegetation? A Systematic Review.” Environmental Evidence 4 (1): 4.

Bertness, M, and R Callaway. 1994. “Positive Interactions in Communities.” Trends in Ecology & Evolution 9 (5): 191–93.

Borenstein, Michael, Larry V Hedges, Julian P T Higgins, and Hannah R Rothstein. 2011. Introduction to Meta-Analysis. John Wiley & Sons.

Choler, P, R Michalet, and R M Callaway. 2001. “Facilitation and Competition on Gradients in Alpine Plant Communities.” Ecology 82 (12): 3295–3308.

Collins, S L, A K Knapp, J M B Riggs, J M Blair, and E M Steinauer. 1998. “Modulation of Diversity by Grazing and Mowing in Native Tallgrass Prairie.” Science 280 (5364): 745–47.

Diaz, Sandra, Imanuel Noy-Meir, and Marcelo Cabido. 2001. “Can Grazing Response of Herbaceous Plants Be Predicted from Simple Vegetative Traits?” J. Appl. Ecol. 38 (3): 497–508.

Elmendorf, Sarah C, Gregory H R Henry, Robert D Hollister, Robert G Björk, Anne D Bjorkman, Terry V Callaghan, Laura Siegwart Collier, et al. 2012. “Global Assessment of Experimental Climate Warming on Tundra Vegetation: Heterogeneity over Space and Time.” Ecology Letters 15 (2): 164–75.

Emmett, Bridget A, Claus Beier, Marc Estiarte, Albert Tietema, Hanne L Kristensen, Dylan Williams, Josep Peñuelas, Inger Schmidt, and Alwyn Sowerby. 2004. “The Response of Soil Processes to Climate Change: Results from Manipulation Studies of Shrublands Across an Environmental Gradient.” Ecosystems 7 (6): 625–37.

Foo, Yong Zhi, Rose E O’Dea, Julia Koricheva, Shinichi Nakagawa, and Malgorzata Lagisz. 2021. “A Practical Guide to Question Formation, Systematic Searching and Study Screening for Literature Reviews in Ecology and Evolution.” Methods Ecol. Evol., nos. 2041-210X.13654 (June).

Frank, D A, P M Groffman, R D Evans, and B F Tracy. 2000. “Ungulate Stimulation of Nitrogen Cycling and Retention in Yellowstone Park Grasslands.” Oecologia 123 (1): 116–21.

Frank, Douglas A, Michelle M Kuns, and Daniel R Guido. 2002. “CONSUMER CONTROL OF GRASSLAND PLANT PRODUCTION.” Ecology 83 (3): 602–6.

Gao, Junjing, and Yohay Carmel. 2020. “A Global Meta-Analysis of Grazing Effects on Plant Richness.” Agric. Ecosyst. Environ. 302 (October): 107072.

Gao, Y, M Schumann, H Chen, N Wu, and others. 2009. “Impacts of Grazing Intensity on Soil Carbon and Nitrogen in an Alpine Meadow on the Eastern Tibetan Plateau.” J. Foodservice.

Golluscio, R A, A T Austin, G C G Martínez, M Gonzalez-Polo, and Sala, O. E., & Jackson, R. B. 2009. “Sheep Grazing Decreases Organic Carbon and Nitrogen Pools in the Patagonian Steppe: Combination of Direct and Indirect Effects.” Ecosystems 12 (4): 686–97.

Grime, J P. 1973. “Competitive Exclusion in Herbaceous Vegetation.” Nature 242 (5396): 344–47.

Herrero-Jáuregui, Cristina, and Martín Oesterheld. 2018. “Effects of Grazing Intensity on Plant Richness and Diversity: A Meta-Analysis.” Oikos 127 (6): 757–66.

Hillebrand, Helmut, and Jessica Gurevitch. 2016. “Meta-Analysis and Systematic Reviews in Ecology.” Elsevier Oceanogr. Ser., 1–11.

Hui, A N, and L I Guoqi. 2014. “Differential Effects of Grazing on Plant Functional Traits in the Desert Grassland.” Pol. J. Ecol.

Humbert, Jean-Yves, John M Dwyer, Aline Andrey, and Raphaël Arlettaz. 2016. “Impacts of Nitrogen Addition on Plant Biodiversity in Mountain Grasslands Depend on Dose, Application Duration and Climate: A Systematic Review.” Glob. Chang. Biol. 22 (1): 110–20.

IPBES. 2018. “Summary for Policymakers of the Regional Assessment Report on Biodiversity and Ecosystem Services for Europe and Central Asia of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.” Edited by M. Fischer, M. Rounsevell, A. Torre-Marin Rando, A. Mader, A. Church, M. Elbakidze, V. Elias, T. Hahn. P.A. Harrison, J. Hauck, B. Martín-López, I. Ring, C. Sandström, I. Sousa Pinto, P. Visconti, N.E. Zimmermann and M. Christie (eds.). IPBES secretariat, Bonn, Germany.

IPCC. 2014. “Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.” Edited by Pachauri, R.K. and Meyer, L.A. (eds.). IPCC, Geneva, Switzerland.

Jun, W, S Liqing, L Jianzhou, and F Zhili. 2008. “CO2 Efflux Under Different Grazing Managements on Subalpine Meadows of Shangri-La, Northwest Yunnan Province, China.” Acta Ecol. Sin. 28 (8): 3574–83.

Karger, Dirk Nikolaus, Olaf Conrad, Jürgen Böhner, Tobias Kawohl, Holger Kreft, Rodrigo Wilber Soria-Auza, Niklaus E Zimmermann, H Peter Linder, and Michael Kessler. 2017. “Climatologies at High Resolution for the Earth’s Land Surface Areas.” Sci Data 4 (September): 170122.

Kato, T, Y Tang, S Gu, X Cui, M Hirota, M Du, Y Li, X Zhao, and T Oikawa. 2004. “Carbon Dioxide Exchange Between the Atmosphere and an Alpine Meadow Ecosystem on the Qinghai–Tibetan Plateau, China.” Agric. For. Meteorol. 124 (1): 121–34.

Klanderud, Kari. 2008. “Species-Specific Responses of an Alpine Plant Community Under Simulated Environmental Change.” J. Veg. Sci. 19 (3): 363–72.

Klein, Julia A, John Harte, and Xin-Quan Zhao. 2005. “Dynamic and Complex Microclimate Responses to Warming and Grazing Manipulations.” Glob. Chang. Biol. 11 (9): 1440–51.

Körner, M, and C Oshawa. 2005. “Mountain Systems.” In Ecosystems and Human Well-Being: Current State and Trends, edited by N Ash. Island Press, Washington, D.C.

Lai, Liming, and Sandeep Kumar. 2020. “A Global Meta-Analysis of Livestock Grazing Impacts on Soil Properties.” PLoS One 15 (8): e0236638.

Lavorel, Sandra, Sandra Díaz, J Hans C Cornelissen, Eric Garnier, Sandy P Harrison, Sue McIntyre, Juli G Pausas, Natalia Pérez-Harguindeguy, Catherine Roumet, and Carlos Urcelay. 2007. “Plant Functional Types: Are We Getting Any Closer to the Holy Grail?” In Terrestrial Ecosystems in a Changing World, edited by Josep G Canadell, Diane E Pataki, and Louis F Pitelka, 149–64. Berlin, Heidelberg: Springer Berlin Heidelberg.

Lu, Xuyang, Kathy C Kelsey, Yan Yan, Jian Sun, Xiaodan Wang, Genwei Cheng, and Jason C Neff. 2017. “Effects of Grazing on Ecosystem Structure and Function of Alpine Grasslands in Qinghai-Tibetan Plateau: A Synthesis.” Ecosphere 8 (1): e01656.

Martinsen, V, J Mulder, G Austrheim, and A Mysterud. 2011. “Carbon Storage in Low-Alpine Grassland Soils: Effects of Different Grazing Intensities of Sheep.” Eur. J. Soil Sci. 62 (6): 822–33.

McNaughton, S J. 1979. “Grazing as an Optimization Process: Grass-Ungulate Relationships in the Serengeti.” Am. Nat. 113 (5): 691–703.

McSherry, Megan E, and Mark E Ritchie. 2013. “Effects of Grazing on Grassland Soil Carbon: A Global Review.” Glob. Chang. Biol. 19 (5): 1347–57.

Merunkova, Kristina, Zdenka Preislerova, Milan Chytr\‘y, and Others. 2012. “White Carpathian Grasslands: Can Local Ecological Factors Explain Their Extraordinary Species Richness?” Preslia 84 (2): 311–25.

Milchunas, D G, and W K Lauenroth. 1993. “Quantitative Effects of Grazing on Vegetation and Soils over a Global Range of Environments.” Ecol. Monogr. 63 (4): 327–66.

Noy-Meir, Imanuel. 1975. “Stability of Grazing Systems: An Application of Predator-Prey Graphs.” J. Ecol. 63 (2): 459–81.

Odriozola, Iñaki, Gonzalo García-Baquero, Marie-Josée Fortin, Nere Amaia Laskurain, and Arantza Aldezabal. 2017. “Grazing Exclusion Unleashes Competitive Plant Responses in Iberian Atlantic Mountain Grasslands.” Appl. Veg. Sci. 20 (1): 50–61.

Olofsson, J, S Stark, and L Oksanen. 2004. “Reindeer Influence on Ecosystem Processes in the Tundra.” Oikos 105 (2): 386–96.

Owensby, Clenton E, Jay M Ham, and Lisa M Auen. 2006. “Fluxes of CO2 from Grazed and Ungrazed Tallgrass Prairie.” Rangeland Ecol. Manage. 59 (2): 111–27.

Pacifici, M, W B Foden, P Visconti, J E M Watson, S H M Butchart, K M Kovacs, B R Scheffers, et al. 2015. “Assessing Species Vulnerability to Climate Change.” Nat. Clim. Chang. 5 (February): 215–24.

Pick, Joel L, Shinichi Nakagawa, and Daniel W A Noble. 2018. “Reproducible, Flexible and High-Throughput Data Extraction from Primary Literature: The metaDigitise R Package.” Methods Ecol. Evol. 10 (3): 426–31.

Poschlod, P, S Kiefer, U Tränkle, S Fischer, and S Bonn. 1998. “Plant Species Richness in Calcareous Grasslands as Affected by Dispersability in Space and Time.” Appl. Veg. Sci. 1 (1): 75–91.

Pullin, Andrew S, and Gavin B Stewart. 2006. “Guidelines for Systematic Review in Conservation and Environmental Management.” Conserv. Biol. 20 (6): 1647–56.

R Core Team. 2020. “R: A Language and Environment for Statistical Computing.” Vienna, Austria.

Sagar, R, G Y Li, J S Singh, and Shiqiang Wan. 2019. “Carbon Fluxes and Species Diversity in Grazed and Fenced Typical Steppe Grassland of Inner Mongolia, China.” J. Plant Ecol. 12 (1): 10–22.

Schimel, David, Timothy G F Kittel, Steven Running, Russell Monson, Andrew Turnipseed, and Dean Anderson. 2002. “Carbon Sequestration Studied in Western US Mountains.” Eos Trans. Amer. Geophys. Union 83 (40): 445–49.

Schmitt, M, M Bahn, G Wohlfahrt, U Tappeiner, and A Cernusca. 2010. “Land Use Affects the Net Ecosystem CO(2) Exchange and Its Components in Mountain Grasslands.” Biogeosciences 7 (8): 2297–2309.

Semmartin, María, Martín R Aguiar, Roberto A Distel, Alicia S Moretto, and Claudio M Ghersa. 2004. “Litter Quality and Nutrient Cycling Affected by Grazing-Induced Species Replacements Along a Precipitation Gradient.” Oikos 107 (1): 148–60.

Shao, C, J Chen, L Li, and L Zhang. 2012. “Ecosystem Responses to Mowing Manipulations in an Arid Inner Mongolia Steppe: An Energy Perspective.” J. Arid Environ. 82 (July): 1–10.

Sjögersten, S, C Alewell, L Cécillon, F Hagedorn, R Jandl, J Leifeld, V Martinsen, A Schindlbacher, M-T Sebastià, and H Van Miegroet. 2011. “Mountain Soils in a Changing Climate - Vulnerability of Carbon Stocks and Ecosystem Feedbacks.” In Soil Carbon in Sensitive European Ecosystems, edited by R Jandl, M Rodeghiero, and M Olsson, 6:118–48. Ecological Studies. Chichester, UK: John Wiley & Sons, Ltd.

Smith, S W, D Johnson, Slo Quin, K Munro, R J Pakeman, R van der Wal, and S J Woodin. 2015. “Combination of Herbivore Removal and Nitrogen Deposition Increases Upland Carbon Storage.” Glob. Chang. Biol. 21 (8): 3036–48.

Steffens, Markus, Angelika Kölbl, Kai Uwe Totsche, and Ingrid Kögel-Knabner. 2008. “Grazing Effects on Soil Chemical and Physical Properties in a Semiarid Steppe of Inner Mongolia (P.R. China).” Geoderma 143 (1): 63–72.

Sun, D S, K Wesche, D D Chen, S H Zhang, G L Wu, G Z Du, and N B Comerford. 2011. “Grazing Depresses Soil Carbon Storage Through Changing Plant Biomass and Composition in a Tibetan Alpine Meadow.” Plant Soil Environ. 57 (6): 271–78.

Tang, Shiming, Kun Wang, Yangzhou Xiang, Dashuan Tian, Jinsong Wang, Yanshu Liu, Bo Cao, Ding Guo, and Shuli Niu. 2019. “Heavy Grazing Reduces Grassland Soil Greenhouse Gas Fluxes: A Global Meta-Analysis.” Sci. Total Environ. 654 (March): 1218–24.

Tälle, Malin, Balázs Deák, Peter Poschlod, Orsolya Valkó, Lars Westerberg, and Per Milberg. 2016. “Grazing Vs. Mowing: A Meta-Analysis of Biodiversity Benefits for Grassland Management.” Agric. Ecosyst. Environ. 222 (April): 200–212.

Wardle, David A, Richard D Bardgett, John N Klironomos, Heikki Setälä, Wim H van der Putten, and Diana H Wall. 2004. “Ecological Linkages Between Aboveground and Belowground Biota.” Science 304 (5677): 1629–33.

Welker, J M, J T Fahnestock, K L Povirk, and others. 2004. “Alpine Grassland CO2 Exchange and Nitrogen Cycling: Grazing History Effects, Medicine Bow Range, Wyoming, USA.” Arct. Antarct. Alp. Res.

Xiong, Dingpeng, Peili Shi, Xianzhou Zhang, and Chris B Zou. 2016. “Effects of Grazing Exclusion on Carbon Sequestration and Plant Diversity in Grasslands of China—A Meta-Analysis.” Ecol. Eng. 94 (September): 647–55.

Yan, Liang, Guangsheng Zhou, and Feng Zhang. 2013. “Effects of Different Grazing Intensities on Grassland Production in China: A Meta-Analysis.” PLoS One 8 (12): e81466.

Ylänne, Henni, and Sari Stark. 2019. “Distinguishing Rapid and Slow C Cycling Feedbacks to Grazing in Sub-Arctic Tundra.” Ecosystems 22 (5): 1145–59.

Zeeman, Matthias J, Rebecca Hiller, Anna K Gilgen, Pavel Michna, Peter Plüss, Nina Buchmann, and Werner Eugster. 2010. “Management and Climate Impacts on Net CO2 Fluxes and Carbon Budgets of Three Grasslands Along an Elevational Gradient in Switzerland.” Agric. For. Meteorol. 150 (4): 519–30.

Zhan, Tianyu, Zhenchao Zhang, Jian Sun, Miao Liu, Xubo Zhang, Fei Peng, Atsushi Tsunekawa, Huakun Zhou, Xiaowei Gou, and Shun Fu. 2020. “Meta-Analysis Demonstrating That Moderate Grazing Can Improve the Soil Quality Across China’s Grassland Ecosystems.” Appl. Soil Ecol. 147 (March): 103438.

Zhang, T, Y Zhang, Mingjie Xu, J Zhu, M C Wimberly, G Yu, S Niu, Y Xi, X Zhang, and J Wang. 2015. “Light-Intensity Grazing Improves Alpine Meadow Productivity and Adaption to Climate Change on the Tibetan Plateau.” Sci. Rep. 5 (October).

Zhu, Xiaoxue, Caiyun Luo, Shiping Wang, Zhenhua Zhang, Shujuan Cui, Xiaoying Bao, Lili Jiang, et al. 2015. “Effects of Warming, Grazing/Cutting and Nitrogen Fertilization on Greenhouse Gas Fluxes During Growing Seasons in an Alpine Meadow on the Tibetan Plateau.” Agric. For. Meteorol. 214-215 (December): 506–14.

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